{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from netCDF4 import Dataset\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.colors as Normalize\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "import matplotlib.ticker as mticker\n",
    "import matplotlib\n",
    "import xarray as xr\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "import dask\n",
    "import os\n",
    "import re\n",
    "import warnings\n",
    "import gc\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import LinearSegmentedColormap, TwoSlopeNorm\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.patheffects as pe\n",
    "\n",
    "\n",
    "import wrf\n",
    "from wrf import (getvar, interplevel, to_np, latlon_coords, get_cartopy,\n",
    "                 cartopy_xlim, cartopy_ylim)\n",
    "\n",
    "import pyart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "month='06'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "####### Read in MRMS data ######\n",
    "directory= f'/pscratch/sd/d/dbrooks/acc2017_analysis/model_evaluation/mrms_data/{month}'\n",
    "mrms_files = sorted(glob.glob(os.path.join(directory, f\"mrms_gauge_corrected_*.nc\")))\n",
    "\n",
    "def downscsale_precip(ds):\n",
    "    # Assuming `ds` is your xarray dataset\n",
    "    ds_downscaled = ds.coarsen(\n",
    "        latitude=2,  # Downsampling factor of 2 (to get resolution of 2km)\n",
    "        longitude=2,   \n",
    "        boundary=\"trim\"\n",
    "    ).mean()  \n",
    "\n",
    "    return ds_downscaled\n",
    "\n",
    "datasets=[]\n",
    "for file in mrms_files:\n",
    "    ds = xr.open_dataset(file)\n",
    "    print(file)\n",
    "    #ds = downscsale_precip(ds)\n",
    "    datasets.append(ds)\n",
    "\n",
    "mrms_ds = xr.concat(datasets, dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 18GB\n",
      "Dimensions:         (time: 720, latitude: 2008, longitude: 3098)\n",
      "Coordinates:\n",
      "  * time            (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "  * latitude        (latitude) float64 16kB 26.34 26.35 26.36 ... 46.4 46.41\n",
      "  * longitude       (longitude) float64 25kB -113.0 -113.0 ... -82.03 -82.02\n",
      "    step            timedelta64[ns] 8B 00:00:00\n",
      "    heightAboveSea  float64 8B 0.0\n",
      "    valid_time      (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "Data variables:\n",
      "    unknown         (time, latitude, longitude) float32 18GB -3.0 -3.0 ... 0.0\n",
      "Attributes:\n",
      "    GRIB_edition:            2\n",
      "    GRIB_centre:             161\n",
      "    GRIB_centreDescription:  US NOAA Office of Oceanic and Atmospheric Research\n",
      "    GRIB_subCentre:          0\n",
      "    Conventions:             CF-1.7\n",
      "    institution:             US NOAA Office of Oceanic and Atmospheric Research\n",
      "    history:                 2025-03-31T13:51 GRIB to CDM+CF via cfgrib-0.9.1...\n"
     ]
    }
   ],
   "source": [
    "print(mrms_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "########## Read in WRF Precip ############\n",
    "filename = f'/pscratch/sd/d/dbrooks/acc2017_analysis/precip_data/current/hourly_precip_data_month{month}.nc'\n",
    "precip_current_ds = xr.open_dataset(filename)\n",
    "\n",
    "time_values = precip_current_ds.Time.values\n",
    "\n",
    "# Adjust only the first time value by subtracting 1 hour\n",
    "time_values[0] = pd.Timestamp(time_values[0]) - pd.Timedelta(hours=1)\n",
    "\n",
    "# Reassign the modified time back to the dataset\n",
    "precip_current_ds = precip_current_ds.assign_coords(Time=time_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 4GB\n",
      "Dimensions:  (south_north: 1080, west_east: 1210, Time: 720)\n",
      "Coordinates:\n",
      "  * Time     (Time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "    XLAT     (south_north, west_east) float32 5MB ...\n",
      "    XLONG    (south_north, west_east) float32 5MB ...\n",
      "Dimensions without coordinates: south_north, west_east\n",
      "Data variables:\n",
      "    RAINNC   (south_north, west_east, Time) float32 4GB ...\n"
     ]
    }
   ],
   "source": [
    "print(precip_current_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4478964480\n"
     ]
    }
   ],
   "source": [
    "print(mrms_ds['unknown'].size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from shapely.geometry import Point, MultiPoint\n",
    "from shapely.prepared import prep\n",
    "\n",
    "# Step 1: Boolean mask for ds1 footprint (first time slice)\n",
    "mask1_bool = ~np.isnan(precip_current_ds.RAINNC.isel(Time=0))\n",
    "\n",
    "# Step 2: Extract lat/lon arrays from ds1\n",
    "lat1 = precip_current_ds.XLAT.data[mask1_bool.data]\n",
    "lon1 = precip_current_ds.XLONG.data[mask1_bool.data]\n",
    "\n",
    "# Step 3: Build polygon from ds1 valid points\n",
    "points = MultiPoint(list(zip(lon1, lat1)))\n",
    "polygon = points.convex_hull  # or cascaded_union for exact pixel shapes\n",
    "prep_poly = prep(polygon)  # speeds up \"contains\" checks\n",
    "\n",
    "# Step 4: Build mask for ds2\n",
    "lat2, lon2 = np.meshgrid(mrms_ds.latitude.data, mrms_ds.longitude.data, indexing='ij')\n",
    "mask2 = np.vectorize(lambda lo, la: prep_poly.contains(Point(lo, la)))(lon2, lat2)\n",
    "\n",
    "# Step 5: Apply mask to ds2\n",
    "# mask2 has shape (len(latitude), len(longitude)), broadcast to ds2\n",
    "mrms_ds = mrms_ds.where(mask2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 18GB\n",
      "Dimensions:         (time: 720, latitude: 2008, longitude: 3098)\n",
      "Coordinates:\n",
      "  * time            (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "  * latitude        (latitude) float64 16kB 26.34 26.35 26.36 ... 46.4 46.41\n",
      "  * longitude       (longitude) float64 25kB -113.0 -113.0 ... -82.03 -82.02\n",
      "    step            timedelta64[ns] 8B 00:00:00\n",
      "    heightAboveSea  float64 8B 0.0\n",
      "    valid_time      (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "Data variables:\n",
      "    unknown         (time, latitude, longitude) float32 18GB nan nan ... nan nan\n",
      "Attributes:\n",
      "    GRIB_edition:            2\n",
      "    GRIB_centre:             161\n",
      "    GRIB_centreDescription:  US NOAA Office of Oceanic and Atmospheric Research\n",
      "    GRIB_subCentre:          0\n",
      "    Conventions:             CF-1.7\n",
      "    institution:             US NOAA Office of Oceanic and Atmospheric Research\n",
      "    history:                 2025-03-31T13:51 GRIB to CDM+CF via cfgrib-0.9.1...\n"
     ]
    }
   ],
   "source": [
    "#mrms_ds = mrms_ds.compute()\n",
    "print(mrms_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done1\n",
      "done2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_precip_current = precip_current_ds['RAINNC'].where(precip_current_ds['RAINNC'] >= 0.25)\n",
    "del precip_current_ds\n",
    "gc.collect()  # Force garbage collection\n",
    "print('done1')\n",
    "all_precip_mrms = mrms_ds['unknown'].where(mrms_ds['unknown'] >= 0.25)\n",
    "print('done2')\n",
    "del mrms_ds  # Delete dataset when done\n",
    "gc.collect()  # Force garbage collection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'unknown' (time: 720, latitude: 2008, longitude: 3098)> Size: 18GB\n",
      "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        ...,\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan]],\n",
      "\n",
      "       [[nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        ...,\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan]],\n",
      "\n",
      "       [[nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        ...,\n",
      "...\n",
      "        ...,\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan]],\n",
      "\n",
      "       [[nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        ...,\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan]],\n",
      "\n",
      "       [[nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        ...,\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan],\n",
      "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)\n",
      "Coordinates:\n",
      "  * time            (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "  * latitude        (latitude) float64 16kB 26.34 26.35 26.36 ... 46.4 46.41\n",
      "  * longitude       (longitude) float64 25kB -113.0 -113.0 ... -82.03 -82.02\n",
      "    step            timedelta64[ns] 8B 00:00:00\n",
      "    heightAboveSea  float64 8B 0.0\n",
      "    valid_time      (time) datetime64[ns] 6kB 2017-06-01 ... 2017-06-30T23:00:00\n",
      "Attributes: (12/30)\n",
      "    GRIB_paramId:                             0\n",
      "    GRIB_dataType:                            ra\n",
      "    GRIB_numberOfPoints:                      24500000\n",
      "    GRIB_typeOfLevel:                         heightAboveSea\n",
      "    GRIB_stepUnits:                           1\n",
      "    GRIB_stepType:                            instant\n",
      "    ...                                       ...\n",
      "    GRIB_name:                                unknown\n",
      "    GRIB_shortName:                           unknown\n",
      "    GRIB_units:                               unknown\n",
      "    long_name:                                unknown\n",
      "    units:                                    unknown\n",
      "    standard_name:                            unknown\n"
     ]
    }
   ],
   "source": [
    "print(all_precip_mrms)\n",
    "#mrms_ds = ds2_masked\n",
    "#print(mrms_ds['unknown'].count().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmweather\n",
    "wind_cmap = cmweather.cm_colorblind.ChaseSpectral\n",
    "\n",
    "p_clevs=[50,100,150,200,250,300,400,500,600]\n",
    "p_cmap = mcolors.ListedColormap(wind_cmap(np.linspace(0.15,0.9,len(p_clevs))))\n",
    "p_norm = mcolors.BoundaryNorm(p_clevs, len(p_clevs))\n",
    "#norm = mcolors.BoundaryNorm(clevs, cmap.N)\n",
    "p_cmap.set_over('#fdd8fb')\n",
    "p_cmap.set_under('white')\n",
    "\n",
    "def plot_precip(ds, wrf=True, month=month):\n",
    "    # Create the plot\n",
    "    fig, ax_wind = plt.subplots(\n",
    "        figsize=(5, 5),\n",
    "        subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
    "    )\n",
    "    if wrf == True:\n",
    "        lat=ds['XLAT']\n",
    "        lon=ds['XLONG']\n",
    "        precip=ds['RAINNC'].where(ds['RAINNC'] >= 0.25)\n",
    "        precip=precip.sum(dim='Time',skipna=True)\n",
    "        title='Current'\n",
    "        precip_spatial_mean = precip.mean(dim=['south_north', 'west_east'],skipna=True)\n",
    "        precip_spatial_max = precip.max(dim=['south_north', 'west_east'])\n",
    "    else:\n",
    "        lat=ds['latitude']\n",
    "        lon=ds['longitude']\n",
    "        precip=ds['unknown'].where(ds['unknown'] >= 0.25)\n",
    "        precip=precip.sum(dim='time',skipna=True)\n",
    "        title='MRMS'\n",
    "        precip_spatial_mean = precip.mean(dim=['latitude', 'longitude'],skipna=True)\n",
    "        precip_spatial_max = precip.max(dim=['latitude', 'longitude'])\n",
    "\n",
    "    #time = datetime(time)\n",
    "\n",
    "    #ax_wind.set_extent([-97.953608,-88.879741,28,32.545423])\n",
    "\n",
    "    if month == '04':\n",
    "        month = 'April'\n",
    "    elif month == '05':\n",
    "        month = 'May'\n",
    "    elif month == '06':\n",
    "        month = 'June'\n",
    "    \n",
    "    #ax_wind = axs[0]\n",
    "    # Plot the reflectivity data\n",
    "    pb = ax_wind.pcolormesh(\n",
    "        lon,\n",
    "        lat,\n",
    "        precip,\n",
    "        cmap=p_cmap,\n",
    "        norm=p_norm,\n",
    "        transform=ccrs.PlateCarree()\n",
    "    )\n",
    "    \n",
    "    # Add features and title\n",
    "    ax_wind.add_feature(cfeature.STATES, edgecolor=\"black\", linewidths=0.85, alpha=0.5, zorder=1)\n",
    "    #ax_wind.add_feature(cfeature.STATES, edgecolor=\"white\", linewidths=0.85, alpha=1, zorder=1)\n",
    "    ax_wind.add_feature(cfeature.COASTLINE, edgecolor=\"black\", linewidths=0.85, alpha=0.5)\n",
    "    #ax_wind.add_feature(USCOUNTIES, linewidth=0.35, alpha=0.3)\n",
    "    gl = ax_wind.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--', zorder=2)\n",
    "    gl.top_labels = False\n",
    "    gl.right_labels = False\n",
    "    if wrf==True:\n",
    "        gl.left_labels = False\n",
    "    else:\n",
    "        gl.left_labels = True\n",
    "    gl.bottom_labels = False\n",
    "    ax_wind.set_title(f'Total Precipitation (mm)', loc='left', fontsize=12)\n",
    "    ax_wind.set_title(f'({title}, {month})', loc='right', fontsize=12, weight='bold')\n",
    "\n",
    "    text_str = (\n",
    "    f\"Mean: {precip_spatial_mean:.2f} mm\\n\"\n",
    "    f\"Max: {precip_spatial_max:.2f} mm\"\n",
    "    )\n",
    "    ax_wind.text(\n",
    "        -112.2, 27.2, text_str,  # Adjust coordinates as needed\n",
    "        fontsize=8, color=\"black\", weight='bold',transform=ccrs.PlateCarree(),\n",
    "        bbox=dict(facecolor=\"white\", alpha=0.9, boxstyle=\"round,pad=0.5\")\n",
    "    )\n",
    "    \n",
    "    # Colorbar for the left plot\n",
    "    #cbar = plt.colorbar(pb, ax=ax_wind, orientation='vertical', fraction=0.05, pad=0.01, shrink=0.6, extend='both', drawedges=True)\n",
    "    #cbar.set_label('mm')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "#plot_precip(mrms_ds, wrf=False)\n",
    "#plot_precip(precip_current_ds, wrf=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Constants\n",
    "PRECIP_BINS = [(0.25, 2.5), (2.5, 10), (10, 50),(50, np.inf)]\n",
    "BIN_LABELS = ['0.25-2.5', '2.5-10', '10-50','≥50']\n",
    "\n",
    "# Function to calculate frequency (number of occurrences) per intensity bin\n",
    "def calculate_percentage_per_bin(precip_data, bins):\n",
    "    total_occurrences = precip_data.count().item()  # Total number of grid points across all time steps\n",
    "    bin_percentages = []\n",
    "\n",
    "    for lower, upper in bins:\n",
    "        bin_mask = (precip_data >= lower) & (precip_data < upper)\n",
    "        bin_frequency = bin_mask.sum().item()  # Count occurrences in this bin\n",
    "        bin_percentage = (bin_frequency / total_occurrences) * 100  # Normalize to percentage\n",
    "        bin_percentages.append(bin_percentage)\n",
    "\n",
    "    return bin_percentages\n",
    "\n",
    "# Functions to calculate relative changes\n",
    "def calculate_relative_change(mrms, current):\n",
    "    return ((current / mrms) - 1) * 100\n",
    "\n",
    "def plot_precip_intensity_frequency_bar(month=month, all_precip_current=all_precip_current, all_precip_mrms=all_precip_mrms):\n",
    "    # Step 1: Use all occurrences of precipitation values for each simulation across all time steps and grid cells\n",
    "    #all_precip_current = precip_current_ds['RAINNC'].where(precip_current_ds['RAINNC'] >= 0.25)\n",
    "    #all_precip_mrms = mrms_ds['unknown'].where(mrms_ds['unknown'] >= 0.25)\n",
    "\n",
    "\n",
    "    # Step 2: Calculate frequency for each bin for each simulation\n",
    "    freq_current = calculate_percentage_per_bin(all_precip_current, PRECIP_BINS)\n",
    "    freq_mrms = calculate_percentage_per_bin(all_precip_mrms, PRECIP_BINS)\n",
    "\n",
    "    relative_change_future = calculate_relative_change(np.array(freq_mrms), np.array(freq_current))\n",
    "\n",
    "    # Step 4: Plotting\n",
    "    fig, ax1 = plt.subplots(figsize=(4, 4))\n",
    "    #ax2 = ax1.twinx()\n",
    "    #ax2.grid(True, axis='y', alpha=0.5, zorder=0)\n",
    "\n",
    "    # Bar width and positions\n",
    "    bar_width = 0.3\n",
    "    x = np.arange(len(PRECIP_BINS))\n",
    "\n",
    "    # Plot bars for each simulation\n",
    "    ax1.bar(x+bar_width/2, freq_mrms, width=bar_width, color='gray', label='MRMS',zorder=2)\n",
    "    ax1.bar(x - bar_width/2, freq_current, width=bar_width, color='black', label='Current',zorder=2)\n",
    "\n",
    "    # Primary Y-axis (left)\n",
    "    ax1.set_xlabel('Precipitation rate (mm hr$^{-1}$)')\n",
    "    ax1.set_ylabel('Percentage of Total Occurrences (%)')\n",
    "    ax1.set_yscale('log')\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(BIN_LABELS)\n",
    "    ax1.minorticks_off()\n",
    "    ax1.set_ylim(0, 500)  # Since we're dealing with percentages\n",
    "    #ax1.set_ylim(10e1, 10e8)\n",
    "\n",
    "    # Add legend for secondary y-axis lines\n",
    "    ax1.legend(loc='upper right')\n",
    "\n",
    "    '''\n",
    "    ax2.plot(x, relative_change_future, color='blue', marker='o', linestyle='--', label='% Change', linewidth=2)\n",
    "    ax2.set_ylabel('Relative change (%)', color='black')\n",
    "    ax2.set_ylim(-100, 20000)  # Adjust based on expected range of relative changes\n",
    "    ax2.set_yscale('symlog', linthresh=10)\n",
    "    custom_ticks = [-100,-10,0, 10,100,1000]\n",
    "    ax2.minorticks_off()\n",
    "    ax2.set_yticks(custom_ticks)\n",
    "    ax2.legend(loc='upper left')\n",
    "    '''\n",
    "    if month == '04':\n",
    "        month = 'April'\n",
    "    elif month == '05':\n",
    "        month = 'May'\n",
    "    elif month == '06':\n",
    "        month = 'June'\n",
    "\n",
    "    # Title and layout adjustments\n",
    "    ax1.set_title(f'{month}')\n",
    "    fig.tight_layout()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_precip_intensity_frequency_bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}